@misc{exampleErrorBibItem,
      title={A Comprehensive Survey of Continual Learning: Theory, Method and Application},
      author={Liyuan Wang and Xingxing Zhang and Hang Su and Jun Zhu},
      year={2023},
      eprint={2302.00487},
      archivePrefix={arXiv},
      primaryClass={cs.LG},,,
}

@misc{wang2023comprehensive,
      title={A Comprehensive Survey of Continual Learning: Theory, Method and Application}, 
      author={Liyuan Wang and Xingxing Zhang and Hang Su and Jun Zhu},
      year={2023},
      eprint={2302.00487},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{chen2023dynamic,
      title={Dynamic Residual Classifier for Class Incremental Learning}, 
      author={Xiuwei Chen and Xiaobin Chang},
      year={2023},
      eprint={2308.13305},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{dong2023heterogeneous,
      title={Heterogeneous Forgetting Compensation for Class-Incremental Learning}, 
      author={Jiahua Dong and Wenqi Liang and Yang Cong and Gan Sun},
      year={2023},
      eprint={2308.03374},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{li2017learning,
      title={Learning without Forgetting}, 
      author={Zhizhong Li and Derek Hoiem},
      year={2017},
      eprint={1606.09282},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{Gou_2021,
   title={Knowledge Distillation: A Survey},
   volume={129},
   ISSN={1573-1405},
   url={http://dx.doi.org/10.1007/s11263-021-01453-z},
   DOI={10.1007/s11263-021-01453-z},
   number={6},
   journal={International Journal of Computer Vision},
   publisher={Springer Science and Business Media LLC},
   author={Gou, Jianping and Yu, Baosheng and Maybank, Stephen J. and Tao, Dacheng},
   year={2021},
   month=mar, pages={1789–1819} }

@misc{shin2017continual,
      title={Continual Learning with Deep Generative Replay}, 
      author={Hanul Shin and Jung Kwon Lee and Jaehong Kim and Jiwon Kim},
      year={2017},
      eprint={1705.08690},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

@misc{ayub2021eec,
      title={EEC: Learning to Encode and Regenerate Images for Continual Learning}, 
      author={Ali Ayub and Alan R. Wagner},
      year={2021},
      eprint={2101.04904},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{ostapenko2019learning,
      title={Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning}, 
      author={Oleksiy Ostapenko and Mihai Puscas and Tassilo Klein and Patrick Jähnichen and Moin Nabi},
      year={2019},
      eprint={1904.03137},
      archivePrefix={arXiv},
      primaryClass={cs.NE}
}

@misc{kemker2018fearnet,
      title={FearNet: Brain-Inspired Model for Incremental Learning}, 
      author={Ronald Kemker and Christopher Kanan},
      year={2018},
      eprint={1711.10563},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@article{Riemer_Klinger_Bouneffouf_Franceschini_2019,
	title = {Scalable Recollections for Continual Lifelong Learning},
	volume = {33},
	url = {https://ojs.aaai.org/index.php/AAAI/article/view/3935},
	doi = {10.1609/aaai.v33i01.33011352},
	abstractnote = {&lt;p&gt;Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to consider more human-realistic settings. Perhaps the most difficult of these settings is that of continual lifelong learning, where the model must learn online over a continuous stream of non-stationary data. A successful continual lifelong learning system must have three key capabilities: it must &lt;em&gt;learn and adapt&lt;/em&gt; over time, it must &lt;em&gt;not forget&lt;/em&gt; what it has learned, and it must be &lt;em&gt;efficient&lt;/em&gt; in both training time and memory. Recent techniques have focused their efforts primarily on the first two capabilities while questions of efficiency remain largely unexplored. In this paper, we consider the problem of efficient and effective storage of experiences over very large time-frames. In particular we consider the case where typical experiences are &lt;em&gt;O&lt;/em&gt;(&lt;em&gt;n&lt;/em&gt;) bits and memories are limited to &lt;em&gt;O&lt;/em&gt;(&lt;em&gt;k&lt;/em&gt;) bits for &lt;em&gt;k &lt;&lt; n&lt;/em&gt;. We present a novel scalable architecture and training algorithm in this challenging domain and provide an extensive evaluation of its performance. Our results show that we can achieve considerable gains on top of state-of-the-art methods such as GEM. &lt;sup&gt;1&lt;/sup&gt;&lt;/p&gt;},
	number = {01},
	journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
	author = {Riemer, Matthew and Klinger, Tim and Bouneffouf, Djallel and Franceschini, Michele},
	year = {2019},
	month = {Jul.},
	pages = {1352-1359}
}

@misc{rostami2019complementary,
      title={Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay}, 
      author={Mohammad Rostami and Soheil Kolouri and Praveen K. Pilly},
      year={2019},
      eprint={1903.04566},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{lopezpaz2022gradient,
      title={Gradient Episodic Memory for Continual Learning}, 
      author={David Lopez-Paz and Marc'Aurelio Ranzato},
      year={2022},
      eprint={1706.08840},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{chaudhry2019efficient,
      title={Efficient Lifelong Learning with A-GEM}, 
      author={Arslan Chaudhry and Marc'Aurelio Ranzato and Marcus Rohrbach and Mohamed Elhoseiny},
      year={2019},
      eprint={1812.00420},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{Zeng_2019,
	   title={Continual learning of context-dependent processing in neural networks},
	   volume={1},
	   ISSN={2522-5839},
	   url={http://dx.doi.org/10.1038/s42256-019-0080-x},
	   DOI={10.1038/s42256-019-0080-x},
	   number={8},
	   journal={Nature Machine Intelligence},
	   publisher={Springer Science and Business Media LLC},
	   author={Zeng, Guanxiong and Chen, Yang and Cui, Bo and Yu, Shan},
	   year={2019},
	   month=aug, pages={364–372} 
   }

@inproceedings{Guo2022AdaptiveOP, 
	title={Adaptive Orthogonal Projection for Batch and Online Continual Learning}, 
	author={Yiduo Guo and Wenpeng Hu and Dongyan Zhao and Bing Liu}, 
	booktitle={AAAI Conference on Artificial Intelligence}, 
	year={2022}, 
    url={https://api.semanticscholar.org/CorpusID:250290748} 
}

@inproceedings{NEURIPS2020_518a38cc,
	author = {Mirzadeh, Seyed Iman and Farajtabar, Mehrdad and Pascanu, Razvan and Ghasemzadeh, Hassan},
	booktitle = {Advances in Neural Information Processing Systems},
	editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
	pages = {7308--7320},
	publisher = {Curran Associates, Inc.},
	title = {Understanding the Role of Training Regimes in Continual Learning},
	url ={https://proceedings.neurips.cc/paper_files/paper/2020/file/518a38cc9a0173d0b2dc088166981cf8-Paper.pdf},
	volume = {33},
	year = {2020}
}

@misc{mirzadeh2020linear,
      title={Linear Mode Connectivity in Multitask and Continual Learning}, 
      author={Seyed Iman Mirzadeh and Mehrdad Farajtabar and Dilan Gorur and Razvan Pascanu and Hassan Ghasemzadeh},
      year={2020},
      eprint={2010.04495},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{DSH,
  title={Deep supervised hashing for fast image retrieval},
  author={Liu, Haomiao and Wang, Ruiping and Shan, Shiguang and Chen, Xilin},
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  pages={2064--2072},
  year={2016}
}

@inproceedings{DHN,
author = {Zhu, Han and Long, Mingsheng and Wang, Jianmin and Cao, Yue},
title = {Deep hashing network for efficient similarity retrieval},
year = {2016},
booktitle = {Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
pages = {2415–2421},
numpages = {7},
}

@inproceedings{HashNet,
  title={Hashnet: Deep learning to hash by continuation},
  author={Cao, Zhangjie and Long, Mingsheng and Wang, Jianmin and Yu, Philip S},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={5608--5617},
  year={2017}
}

@inproceedings{DCH,
  title={Deep cauchy hashing for hamming space retrieval},
  author={Cao, Yue and Long, Mingsheng and Liu, Bin and Wang, Jianmin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1229--1237},
  year={2018}
}

@inproceedings{DPN,
  title={Deep polarized network for supervised learning of accurate binary hashing codes},
  author={Fan, Lixin and Ng, Kam Woh and Ju, Ce and Zhang, Tianyu and Chan, Chee Seng},
  booktitle={IJCAI},
  pages={825--831},
  year={2020}
}

@inproceedings{CSQ,
  title={Central similarity quantization for efficient image and video retrieval},
  author={Yuan, Li and Wang, Tao and Zhang, Xiaopeng and Tay, Francis EH and Jie, Zequn and Liu, Wei and Feng, Jiashi},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={3083--3092},
  year={2020}
}

@inproceedings{transhash,
  title={Transhash: Transformer-based hamming hashing for efficient image retrieval},
  author={Chen, Yongbiao and Zhang, Sheng and Liu, Fangxin and Chang, Zhigang and Ye, Mang and Qi, Zhengwei},
  booktitle={Proceedings of the 2022 International Conference on Multimedia Retrieval},
  pages={127--136},
  year={2022}
}

 @inproceedings{GreedyHash,
  author = {Su, Shupeng and Zhang, Chao and Han, Kai and Tian, Yonghong},
  title = {Greedy hash: Towards fast optimization for accurate hash coding in cnn},
  booktitle = {Proceedings of the 32nd International Conference on Neural Information Processing Systems},
  year = {2018},
  pages = {806--815}
}

@inproceedings{CNNfield1,
  title={Understanding the effective receptive field in deep convolutional neural networks},
  author={Luo, Wenjie and Li, Yujia and Urtasun, Raquel and Zemel, Richard},
  booktitle={Proceedings of the 30th International Conference on Neural Information Processing Systems},
  pages={4905--4913},
  year={2016}
}

@inproceedings{CNNfield2,
  author = {Zhou, B and Khosla, A and Lapedriza, À and Oliva, A and Torralba, A},
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  year = {2015},
  month = {5},
  pages = {7--9}
}

@article{Survey,
  title={A survey on deep hashing methods},
  author={Luo, Xiao and Wang, Haixin and Wu, Daqing and Chen, Chong and Deng, Minghua and Huang, Jianqiang and Hua, Xian-Sheng},
  journal={ACM Transactions on Knowledge Discovery from Data},
  volume={17},
  number={1},
  pages={1--50},
  year={2023},
  publisher={ACM New York, NY}
}


@inproceedings{conformer,
  title={Conformer: Local features coupling global representations for visual recognition},
  author={Peng, Zhiliang and Huang, Wei and Gu, Shanzhi and Xie, Lingxi and Wang, Yaowei and Jiao, Jianbin and Ye, Qixiang},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={367--376},
  year={2021}
}

@article{imagenet,
  title={Imagenet large scale visual recognition challenge},
  author={Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael},
  journal={International journal of computer vision},
  volume={115},
  pages={211--252},
  year={2015},
  publisher={Springer}
}

@article{CIFAR-10,
  title={Learning multiple layers of features from tiny images},
  author={Krizhevsky, Alex},
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  year={2009}
}

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  title={Modern information retrieval},
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}


@inproceedings{Transformer,
author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, \L{}ukasz and Polosukhin, Illia},
title = {Attention is all you need},
year = {2017},
booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems},
pages = {6000–6010},
}

@article{ViT,
  title={An image is worth 16x16 words: Transformers for image recognition at scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others},
  journal={CoRR},
  volume={abs/2010.11929},
  year={2020}
}

@article{lecun,
  title={Gradient-based learning applied to document recognition},
  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
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}

@article{AlexNet,
  title={Imagenet classification with deep convolutional neural networks},
  author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
  journal={Communications of the ACM},
  volume={60},
  number={6},
  pages={84--90},
  year={2017},
  publisher={AcM New York, NY, USA}
}

@article{VGG,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Karen Simonyan and Andrew Zisserman},
  journal={CoRR},
  year={2014},
  volume={abs/1409.1556}
}

@inproceedings{ResNet,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@article{seg,
  title={Image segmentation using deep learning: A survey},
  author={Minaee, Shervin and Boykov, Yuri and Porikli, Fatih and Plaza, Antonio and Kehtarnavaz, Nasser and Terzopoulos, Demetri},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={44},
  number={7},
  pages={3523--3542},
  year={2021},
  publisher={IEEE}
}

@inproceedings{restoration,
  title={Learning deep CNN denoiser prior for image restoration},
  author={Zhang, Kai and Zuo, Wangmeng and Gu, Shuhang and Zhang, Lei},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3929--3938},
  year={2017}
}

@article{face,
  title={A survey on deep learning based face recognition},
  author={Guo, Guodong and Zhang, Na},
  journal={Computer vision and image understanding},
  volume={189},
  pages={102805},
  year={2019},
  publisher={Elsevier}
}

@inproceedings{swin,
  title={Swin transformer: Hierarchical vision transformer using shifted windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={10012--10022},
  year={2021}
}

@article{diet,
  title={Diet: Lightweight language understanding for dialogue systems},
  author={Bunk, Tanja and Varshneya, Daksh and Vlasov, Vladimir and Nichol, Alan},
  journal={arXiv e-prints},
  pages={arXiv--2004},
  year={2020}
}

@article{detr,
  title={Deformable detr: Deformable transformers for end-to-end object detection},
  author={Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng},
  journal={arXiv e-prints},
  pages={arXiv--2010},
  year={2020}
}

@inproceedings{snow,
author={Webster, Arthur F and Tavares, Stafford E},
title={On the design of S-boxes},
booktitle={Advances in Cryptology --- CRYPTO '85 Proceedings},
year={1986},
pages={523--534}
}

@article{engine,
  title={Image retrieval from the world wide web: Issues, techniques, and systems},
  author={Kherfi, Mohammed Lamine and Ziou, Djemel and Bernardi, Alan},
  journal={ACM Computing Surveys (Csur)},
  volume={36},
  number={1},
  pages={35--67},
  year={2004},
  publisher={ACM New York, NY, USA}
}

@article{facerec,
  title={Joint face detection and alignment using multitask cascaded convolutional networks},
  author={Zhang, Kaipeng and Zhang, Zhanpeng and Li, Zhifeng and Qiao, Yu},
  journal={IEEE signal processing letters},
  volume={23},
  number={10},
  pages={1499--1503},
  year={2016},
  publisher={IEEE}
}

@inproceedings{yolo,
  title={You only look once: Unified, real-time object detection},
  author={Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={779--788},
  year={2016}
}

@article{ns,
  title={An algorithm for finding best matches in logarithmic expected time},
  author={Friedman, Jerome H and Bentley, Jon Louis and Finkel, Raphael Ari},
  journal={ACM Transactions on Mathematical Software (TOMS)},
  volume={3},
  number={3},
  pages={209--226},
  year={1977},
  publisher={ACM New York, NY, USA}
}